---
title: Running Dagster in Docker | Dagster
description: A guide to deploying Dagster in Docker.
---

# Deploying Dagster in Docker

If you are running on AWS ECS or another container-based orchestration system,
you'll likely want to package Dagit using a Docker image.

A minimal skeleton `Dockerfile` that will run Dagit is shown below:

```dockerfile file=/deploying/docker/Dockerfile
FROM python:3.7-slim

RUN mkdir -p /opt/dagster/dagster_home /opt/dagster/app

RUN pip install dagit dagster-postgres

# Copy your pipeline code and workspace to /opt/dagster/app
COPY pipelines.py workspace.yaml /opt/dagster/app/

ENV DAGSTER_HOME=/opt/dagster/dagster_home/

# Copy dagster instance YAML to $DAGSTER_HOME
COPY dagster.yaml /opt/dagster/dagster_home/

WORKDIR /opt/dagster/app

EXPOSE 3000

ENTRYPOINT ["dagit", "-h", "0.0.0.0", "-p", "3000"]
```

You'll also need to include a `dagster.yaml` file in the same directory as the Dockerfile to
configure the [Dagster instance](/overview/instances/dagster-instance) that Dagit will use:

```yaml file=/deploying/docker/dagster.yaml
run_storage:
  module: dagster_postgres.run_storage
  class: PostgresRunStorage
  config:
    postgres_db:
      username:
        env: DAGSTER_PG_USERNAME
      password:
        env: DAGSTER_PG_PASSWORD
      hostname:
        env: DAGSTER_PG_HOST
      db_name:
        env: DAGSTER_PG_DB
      port: 5432

event_log_storage:
  module: dagster_postgres.event_log
  class: PostgresEventLogStorage
  config:
    postgres_db:
      username:
        env: DAGSTER_PG_USERNAME
      password:
        env: DAGSTER_PG_PASSWORD
      hostname:
        env: DAGSTER_PG_HOST
      db_name:
        env: DAGSTER_PG_DB
      port: 5432

schedule_storage:
  module: dagster_postgres.schedule_storage
  class: PostgresScheduleStorage
  config:
    postgres_db:
      username:
        env: DAGSTER_PG_USERNAME
      password:
        env: DAGSTER_PG_PASSWORD
      hostname:
        env: DAGSTER_PG_HOST
      db_name:
        env: DAGSTER_PG_DB
      port: 5432

compute_logs:
  module: dagster_aws.s3.compute_log_manager
  class: S3ComputeLogManager
  config:
    bucket: "mycorp-dagster-compute-logs"
    prefix: "dagster-test-"

local_artifact_storage:
  module: dagster.core.storage.root
  class: LocalArtifactStorage
  config:
    base_dir: "/opt/dagster/local/"
```

In cases where you're using environment variable to configure the instance, you should ensure these
environment variables are exposed in the running Dagit container.

In practice, you may want to volume mount your pipeline code into your containers to enable
deployment patterns such as git-sync sidecars that avoid the need to rebuild images and redeploy
containers when pipeline code changes.

Dagit servers expose a health check endpoint at `/dagit_info`, which returns a JSON response like:

```JSON
{
  "dagit_version": "0.6.6",
  "dagster_graphql_version": "0.6.6",
  "dagster_version": "0.6.6"
}
```

## Multi-container Docker deployment

More advanced dagster deployments will require deploying more than one container.
For example, if you are using [dagster-daemon](/overview/daemon) to run schedules and sensors or manage a queue of runs,
you'll likely want a separate container running the `dagster-daemon` service. Dagster also supports
a deployment setup where pipeline code can be updated and deployed separately in its own container,
without needing to redeploy the other dagster services.

A complete working example of this architecture using docker-compose can be found in
[this example](/examples/deploy_docker), which includes a dagit container, a dagster-daemon
container, and one or more containers with user pipeline code.
